To rank the checkpoints pre-trained over classification source tasks, the recently proposed LEEP [1]} measure is directly applicable despite that it was originally designed for between-task transfer. Denote by \(\mathcal {Z}\)  the classification space of a checkpoint \(\theta \) . We can interpret \(\theta (x)_z\) , the \(z\) -th (softmax) output element, as the probability of classifying the input \(x\)  into the class \(z\in \mathcal {Z}\) . Given a downstream task \(t\sim \mathcal {T}\)  and its test set \(\lbrace (x_j,y_j)\rbrace _{j=1}^n\) , the LEEP ranking score for the checkpoint \(\theta \)  is calculated by
\(\nonumber \mathbf {R}_\textsc {LEEP}(\theta ,t) &:= \frac{1}{n}\sum _{j=1}^n \log P(y_j|x_j,\theta ,t) \\ P(y|x,\theta ,t) &:=\sum _{z\in \mathcal {Z}}\hat{P}(y|z)\theta (x)_z \) 
